Data cleaning and visualizations of a cricket Dataset.ipynb

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   "source": [
    "    Handling missing data is the important step in data preprocessing. Below are the widely used 2 methods which provide for greater flexibility in dealing with missing data dependent on the features of the dataset and the objectives of the study. The one to use is determined by the context, the amount of missing data, and the influence of missing values on the analysis.\n",
    "\n",
    "    1. Missing Values Removal:\n",
    "\n",
    "    One method is to eliminate rows or columns with missing values. This is appropriate when the missing data is minor and has no substantial influence on the analysis. eg- .dropna()\n",
    "\n",
    "    2. Imputation:\n",
    "\n",
    "    Another popular strategy is to substitute guessed or computed values for missing values. This is frequently employed when the missing data is important and eliminating it would result in a severe loss of knowledge. For example: ffill"
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player Code</th>\n",
       "      <th>Player Name</th>\n",
       "      <th>Match Number</th>\n",
       "      <th>Number of Wickets Taken</th>\n",
       "      <th>Number of Runs</th>\n",
       "      <th>Salary Estimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>M5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Total Player Runs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>265.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>7875M11</td>\n",
       "      <td>Raj  Mr. Webb</td>\n",
       "      <td>M1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>$79K-$106K (Glassdoor est.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Gran Total Stats of All the above players</td>\n",
       "      <td>NaN</td>\n",
       "      <td>268.0</td>\n",
       "      <td>9434.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156 rows × 6 columns</p>\n",
       "</div>"
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       "     Player Code                                    Player Name Match Number  \\\n",
       "1             N45234                         Maud Mr. Nicholson           M1   \n",
       "2                NaN                                        NaN           M3   \n",
       "3                NaN                                                      M5   \n",
       "4                NaN                                        NaN           M6   \n",
       "5                NaN                          Total Player Runs          NaN   \n",
       "..               ...                                        ...          ...   \n",
       "218          7875M11                              Raj  Mr. Webb           M1   \n",
       "219              NaN                                        NaN           M2   \n",
       "220              NaN                                        NaN           M3   \n",
       "221              NaN                                        NaN           M4   \n",
       "223              NaN  Gran Total Stats of All the above players          NaN   \n",
       "\n",
       "     Number of Wickets Taken       Number of Runs   \\\n",
       "1                          2.0                82.0   \n",
       "2                          3.0                71.0   \n",
       "3                          0.0                36.0   \n",
       "4                          1.0                76.0   \n",
       "5                          NaN               265.0   \n",
       "..                         ...                 ...   \n",
       "218                        1.0                89.0   \n",
       "219                        4.0                31.0   \n",
       "220                        0.0                92.0   \n",
       "221                        1.0                19.0   \n",
       "223                      268.0              9434.0   \n",
       "\n",
       "                  Salary Estimate  \n",
       "1    $137K-$171K (Glassdoor est.)  \n",
       "2                             NaN  \n",
       "3                             NaN  \n",
       "4                             NaN  \n",
       "5                             NaN  \n",
       "..                            ...  \n",
       "218   $79K-$106K (Glassdoor est.)  \n",
       "219                           NaN  \n",
       "220                           NaN  \n",
       "221                           NaN  \n",
       "223                           NaN  \n",
       "\n",
       "[156 rows x 6 columns]"
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   "source": [
    "## Example for Missing Values Removal\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "drop_blanks = pd.read_csv('Cricket Dataset.csv', skiprows=4)\n",
    "drop_blanks = drop_blanks.dropna( how= 'all',axis = 0 )# Deleting totally blank rows\n",
    "drop_blanks = drop_blanks.dropna( how= 'all',axis = 1 )# Deleting totally blank columns\n",
    "drop_blanks"
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player Code</th>\n",
       "      <th>Player Name</th>\n",
       "      <th>Match Number</th>\n",
       "      <th>Number of Wickets Taken</th>\n",
       "      <th>Number of Runs</th>\n",
       "      <th>Salary Estimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Total Player Runs</td>\n",
       "      <td>M6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>7875M11</td>\n",
       "      <td>Raj  Mr. Webb</td>\n",
       "      <td>M1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>$79K-$106K (Glassdoor est.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>7875M11</td>\n",
       "      <td>Raj  Mr. Webb</td>\n",
       "      <td>M2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>$79K-$106K (Glassdoor est.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>7875M11</td>\n",
       "      <td>Raj  Mr. Webb</td>\n",
       "      <td>M3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>$79K-$106K (Glassdoor est.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>7875M11</td>\n",
       "      <td>Raj  Mr. Webb</td>\n",
       "      <td>M4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>$79K-$106K (Glassdoor est.)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>7875M11</td>\n",
       "      <td>Gran Total Stats of All the above players</td>\n",
       "      <td>M4</td>\n",
       "      <td>268.0</td>\n",
       "      <td>9434.0</td>\n",
       "      <td>$79K-$106K (Glassdoor est.)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>156 rows × 6 columns</p>\n",
       "</div>"
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      "text/plain": [
       "     Player Code                                    Player Name Match Number  \\\n",
       "1             N45234                         Maud Mr. Nicholson           M1   \n",
       "2             N45234                         Maud Mr. Nicholson           M3   \n",
       "3             N45234                         Maud Mr. Nicholson           M5   \n",
       "4             N45234                         Maud Mr. Nicholson           M6   \n",
       "5             N45234                          Total Player Runs           M6   \n",
       "..               ...                                        ...          ...   \n",
       "218          7875M11                              Raj  Mr. Webb           M1   \n",
       "219          7875M11                              Raj  Mr. Webb           M2   \n",
       "220          7875M11                              Raj  Mr. Webb           M3   \n",
       "221          7875M11                              Raj  Mr. Webb           M4   \n",
       "223          7875M11  Gran Total Stats of All the above players           M4   \n",
       "\n",
       "     Number of Wickets Taken       Number of Runs   \\\n",
       "1                          2.0                82.0   \n",
       "2                          3.0                71.0   \n",
       "3                          0.0                36.0   \n",
       "4                          1.0                76.0   \n",
       "5                          1.0               265.0   \n",
       "..                         ...                 ...   \n",
       "218                        1.0                89.0   \n",
       "219                        4.0                31.0   \n",
       "220                        0.0                92.0   \n",
       "221                        1.0                19.0   \n",
       "223                      268.0              9434.0   \n",
       "\n",
       "                  Salary Estimate  \n",
       "1    $137K-$171K (Glassdoor est.)  \n",
       "2    $137K-$171K (Glassdoor est.)  \n",
       "3    $137K-$171K (Glassdoor est.)  \n",
       "4    $137K-$171K (Glassdoor est.)  \n",
       "5    $137K-$171K (Glassdoor est.)  \n",
       "..                            ...  \n",
       "218   $79K-$106K (Glassdoor est.)  \n",
       "219   $79K-$106K (Glassdoor est.)  \n",
       "220   $79K-$106K (Glassdoor est.)  \n",
       "221   $79K-$106K (Glassdoor est.)  \n",
       "223   $79K-$106K (Glassdoor est.)  \n",
       "\n",
       "[156 rows x 6 columns]"
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   "source": [
    "## Example for Imputation\n",
    "\n",
    "drop_blanks=drop_blanks.replace(' ', np.NaN)\n",
    "drop_blanks=drop_blanks.replace('', np.NaN)\n",
    "imputation=drop_blanks.ffill()\n",
    "imputation"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    One of the most crucial aspects of data analysis is dealing with missing data. Missing values can cause bias, weaken statistical power and affect the reliability of results. In this article, we will delve into the significance, techniques, challenges and implications of handling missing data in a real-world data analysis scenario.\n",
    "\n",
    "#### Significance of Handling Missing Data:\n",
    "    1.) Biased Results: Ignoring missing data can lead to biased results, particularly if the missing data is not missing completely at random (MCAR).\n",
    "    \n",
    "    2.) Reduced Statistical Power: Reduced statistical power due to missing data can make it challenging to identify authentic effects during analyses.\n",
    "    \n",
    "    3.) Inaccurate Inferences: Inaccurate inferences about relationships within data can result from mishandling of missing data.\n",
    "\n",
    "#### Techniques for Handling Missing Data:\n",
    "    1.) Deletion Methods: If there are only a few missing values and they are distributed at random, it is possible to remove rows or columns using the dropna() function in Pandas.\n",
    "    \n",
    "    2.) Imputation: Replacing empty values with calculated or estimated values is a common practice. The techniques used for this include filling in with mean, median, or mode imputation, or more advanced strategies such as regression imputation.\n",
    "\n",
    "#### Challenges Posed by Missing Data:\n",
    "    1.) Data Bias: It is possible that the reason for the absence of data is connected with the variable, which can result in bias. For instance, individuals who earn a high income and opt not to disclose it may have missing income data.\n",
    "    \n",
    "    2.) Increased Variability: It is possible that the imputed data's variability is not fully accounted for, which could result in standard errors and confidence intervals that are overly optimistic.\n",
    "\n",
    "#### Potential Implications of Different Imputation Methods:\n",
    "    1.) Mean Imputation:\n",
    "          Pros: Simple and quick.\n",
    "          Cons: If the missing data are not MCAR, this method may not be appropriate. In addition, if missing values are systematically associated with other variables, it has the potential to distort relationships.\n",
    "\n",
    "    2.) Regression Imputation:\n",
    "          Pros: This approach takes into account the relationships with other variables.\n",
    "          Cons: It is assumed that there is a linear relationship, but this assumption may create bias if there is actually a nonlinear relationship.\n",
    "\n",
    "    3.) Multiple Imputation:\n",
    "          Pros: Imputation uncertainty is considered in the accounts.\n",
    "          Cons: It may require significant computational resources to process this task.\n",
    "\n",
    "#### Importance of Assessing and Documenting Handling of Missing Data:\n",
    "    1.) Transparency: Providing documentation on how missing data is handled can improve the transparency of the analysis, enabling others to comprehend and reproduce the procedure.\n",
    "    \n",
    "    2.) Reproducibility: Reproducibility is ensured by maintaining a transparent log of missing data handling, which permits others to validate and expand on the analysis without any issues."
   ]
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   "cell_type": "code",
   "execution_count": 3,
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     "text": [
      "Original DataFrame:\n",
      "     A    B\n",
      "0  1.0  5.0\n",
      "1  2.0  NaN\n",
      "2  NaN  7.0\n",
      "3  4.0  8.0\n",
      "\n",
      "DataFrame after deleting rows with missing values:\n",
      "     A    B\n",
      "0  1.0  5.0\n",
      "3  4.0  8.0\n",
      "\n",
      "DataFrame after imputing with mean:\n",
      "          A         B\n",
      "0  1.000000  5.000000\n",
      "1  2.000000  6.666667\n",
      "2  2.333333  7.000000\n",
      "3  4.000000  8.000000\n",
      "\n",
      "DataFrame after regression imputation:\n",
      "          A         B\n",
      "0  1.000000  5.000000\n",
      "1  2.000000  6.666667\n",
      "2  2.333333  7.000000\n",
      "3  4.000000  8.000000\n"
     ]
    }
   ],
   "source": [
    "# EXAMPLES\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Create a DataFrame with missing values\n",
    "data = {'A': [1, 2, np.nan, 4], 'B': [5, np.nan, 7, 8]}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Scenario 1: Delete Rows with Missing Values\n",
    "df_deleted_rows = df.dropna()\n",
    "\n",
    "# Scenario 2: Impute with Mean\n",
    "df_imputed_mean = df.fillna(df.mean())\n",
    "\n",
    "# Scenario 3: Impute with Regression\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "imputer = SimpleImputer(strategy='mean')\n",
    "reg_imputer = SimpleImputer(strategy='mean')\n",
    "\n",
    "df_imputed_reg = pd.DataFrame(reg_imputer.fit_transform(df), columns=df.columns)\n",
    "\n",
    "print(\"Original DataFrame:\")\n",
    "print(df)\n",
    "\n",
    "print(\"\\nDataFrame after deleting rows with missing values:\")\n",
    "print(df_deleted_rows)\n",
    "\n",
    "print(\"\\nDataFrame after imputing with mean:\")\n",
    "print(df_imputed_mean)\n",
    "\n",
    "print(\"\\nDataFrame after regression imputation:\")\n",
    "print(df_imputed_reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Matplotlib, Seaborn, and Altair are popular Python libraries for data visualization, each with its strengths and use cases. Here are the key differences and basic use cases for each:\n",
    "\n",
    "#### 1. Matplotlib:\n",
    "    1.) Core Library: Matplotlib is a foundational library for 2D and 3D plotting in Python.\n",
    "    2.) Control Over Plotting Details: Provides fine-grained control over the appearance of plots.\n",
    "    3.) Use Cases:\n",
    "            a.) Basic line plots, scatter plots, and bar plots.\n",
    "            b.) Customizing plot details like colors, labels, and markers.\n",
    "            c.) Creating complex plots and subplots.\n",
    "\n",
    "#### 2. Seaborn:\n",
    "    1.)Statistical Data Visualization: Built on top of Matplotlib, Seaborn provides a high-level interface for creating informative statistical graphics.\n",
    "    2.) Attractive Aesthetics: Emphasizes attractive and informative statistical graphics with default color palettes.\n",
    "    3.) Use Cases:\n",
    "            a.) Visualizing statistical relationships (e.g., regression plots, distribution plots).\n",
    "            b.) Heatmaps for correlation matrices.\n",
    "            c.) Categorical data visualization.\n",
    "\n",
    "#### 3. Altair:\n",
    "    1.) Declarative Visualization: Altair is a declarative statistical visualization library, which means you describe the data and the visualizations without worrying about the specifics of how it's rendered.\n",
    "    2.) Interactive Visualizations: Emphasizes creating interactive visualizations with a concise and expressive syntax.\n",
    "    3.) Use Cases:\n",
    "            a.) Exploratory data analysis with interactive charts.\n",
    "            b.) Declarative approach to building complex visualizations.\n",
    "            c.) Embedding interactive charts in web applications.\n",
    "\n",
    "#### Summary:\n",
    "    1.) Matplotlib: A low-level plotting library that allows for substantial customization and is ideal for building complicated graphs and fine-tuning elements.\n",
    "    2.) Seaborn: A higher-level program that concentrates on statistical data visualization, provides appealing default styles, and makes it easier to create complex charts.\n",
    "    3.) Altair : A declarative syntax for interactive visualizations that may be used for exploratory data analysis and embedding interactive charts in online applications.\n",
    "\n",
    "    We need to choose a library based on our requirements, the level of customisation required, and the sort of visualizations we intend to generate. Often, a combination of these libraries might be utilized in a project for various aspects of data visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            Chart with Altair\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  #altair-viz-f1db70437a544787a7dc0cefc7d4a80d.vega-embed {\n",
       "    width: 100%;\n",
       "    display: flex;\n",
       "  }\n",
       "\n",
       "  #altair-viz-f1db70437a544787a7dc0cefc7d4a80d.vega-embed details,\n",
       "  #altair-viz-f1db70437a544787a7dc0cefc7d4a80d.vega-embed details summary {\n",
       "    position: relative;\n",
       "  }\n",
       "</style>\n",
       "<div id=\"altair-viz-f1db70437a544787a7dc0cefc7d4a80d\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-f1db70437a544787a7dc0cefc7d4a80d\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-f1db70437a544787a7dc0cefc7d4a80d\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.15.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.15.1\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"url\": \"https://cdn.jsdelivr.net/npm/vega-datasets@v1.29.0/data/cars.json\"}, \"mark\": {\"type\": \"point\"}, \"encoding\": {\"color\": {\"field\": \"Origin\", \"type\": \"nominal\"}, \"x\": {\"field\": \"Horsepower\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"Miles_per_Gallon\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.15.1.json\"}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EXAMPLES\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import altair as alt\n",
    "import numpy as np\n",
    "from vega_datasets import data\n",
    "\n",
    "x = np.linspace(0, 2 * np.pi, 100)\n",
    "y = np.sin(x)\n",
    "\n",
    "plt.plot(x, y, label='Sine Curve')\n",
    "plt.title('Sine Curve Example')\n",
    "plt.xlabel('X-axis')\n",
    "plt.ylabel('Y-axis')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Load sample dataset\n",
    "tips = sns.load_dataset('tips')\n",
    "\n",
    "# Create a scatter plot\n",
    "sns.scatterplot(x='total_bill', y='tip', data=tips, hue='day', palette='Set1')\n",
    "plt.title('Scatter Plot with Seaborn')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "cars = data.cars.url\n",
    "\n",
    "chart = alt.Chart(cars).mark_point().encode(\n",
    "    x='Horsepower:Q',\n",
    "    y='Miles_per_Gallon:Q',\n",
    "    color='Origin:N',\n",
    ")\n",
    "print('            Chart with Altair')\n",
    "chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player Code</th>\n",
       "      <th>Player Name</th>\n",
       "      <th>Match Number</th>\n",
       "      <th>Number of Wickets Taken</th>\n",
       "      <th>Number of Runs</th>\n",
       "      <th>Salary Estimate</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Title</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Minimum_Salary</th>\n",
       "      <th>Maximum_Salary</th>\n",
       "      <th>Average_Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M1</td>\n",
       "      <td>2</td>\n",
       "      <td>82</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "      <td>Maud</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Nicholson</td>\n",
       "      <td>137</td>\n",
       "      <td>171</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M3</td>\n",
       "      <td>3</td>\n",
       "      <td>71</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "      <td>Maud</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Nicholson</td>\n",
       "      <td>137</td>\n",
       "      <td>171</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M5</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "      <td>Maud</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Nicholson</td>\n",
       "      <td>137</td>\n",
       "      <td>171</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M6</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "      <td>Maud</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Nicholson</td>\n",
       "      <td>137</td>\n",
       "      <td>171</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A4578</td>\n",
       "      <td>Logan Sir. Thomas</td>\n",
       "      <td>M1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>$75K-$131K (Glassdoor est.)</td>\n",
       "      <td>Logan</td>\n",
       "      <td>Sir.</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>75</td>\n",
       "      <td>131</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>7845522</td>\n",
       "      <td>Tori Knowles</td>\n",
       "      <td>M1</td>\n",
       "      <td>2</td>\n",
       "      <td>54</td>\n",
       "      <td>$71K-$123K (Glassdoor est.)</td>\n",
       "      <td>Tori</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Knowles</td>\n",
       "      <td>71</td>\n",
       "      <td>123</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>7875M11</td>\n",
       "      <td>Raj  Mr. Webb</td>\n",
       "      <td>M1</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>$79K-$106K (Glassdoor est.)</td>\n",
       "      <td>Raj</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Webb</td>\n",
       "      <td>79</td>\n",
       "      <td>106</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>7875M11</td>\n",
       "      <td>Raj  Mr. Webb</td>\n",
       "      <td>M2</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>$79K-$106K (Glassdoor est.)</td>\n",
       "      <td>Raj</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Webb</td>\n",
       "      <td>79</td>\n",
       "      <td>106</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>7875M11</td>\n",
       "      <td>Raj  Mr. Webb</td>\n",
       "      <td>M3</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>$79K-$106K (Glassdoor est.)</td>\n",
       "      <td>Raj</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Webb</td>\n",
       "      <td>79</td>\n",
       "      <td>106</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>7875M11</td>\n",
       "      <td>Raj  Mr. Webb</td>\n",
       "      <td>M4</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>$79K-$106K (Glassdoor est.)</td>\n",
       "      <td>Raj</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Webb</td>\n",
       "      <td>79</td>\n",
       "      <td>106</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Player Code         Player Name Match Number Number of Wickets Taken  \\\n",
       "5        N45234  Maud Mr. Nicholson           M1                       2   \n",
       "6        N45234  Maud Mr. Nicholson           M3                       3   \n",
       "7        N45234  Maud Mr. Nicholson           M5                       0   \n",
       "8        N45234  Maud Mr. Nicholson           M6                       1   \n",
       "10        A4578   Logan Sir. Thomas           M1                       0   \n",
       "..          ...                 ...          ...                     ...   \n",
       "220     7845522        Tori Knowles           M1                       2   \n",
       "222     7875M11       Raj  Mr. Webb           M1                       1   \n",
       "223     7875M11       Raj  Mr. Webb           M2                       4   \n",
       "224     7875M11       Raj  Mr. Webb           M3                       0   \n",
       "225     7875M11       Raj  Mr. Webb           M4                       1   \n",
       "\n",
       "    Number of Runs               Salary Estimate First Name Title  Last Name  \\\n",
       "5               82  $137K-$171K (Glassdoor est.)       Maud   Mr.  Nicholson   \n",
       "6               71  $137K-$171K (Glassdoor est.)       Maud   Mr.  Nicholson   \n",
       "7               36  $137K-$171K (Glassdoor est.)       Maud   Mr.  Nicholson   \n",
       "8               76  $137K-$171K (Glassdoor est.)       Maud   Mr.  Nicholson   \n",
       "10               4   $75K-$131K (Glassdoor est.)      Logan  Sir.     Thomas   \n",
       "..             ...                           ...        ...   ...        ...   \n",
       "220             54   $71K-$123K (Glassdoor est.)       Tori  (NA)    Knowles   \n",
       "222             89   $79K-$106K (Glassdoor est.)        Raj   Mr.       Webb   \n",
       "223             31   $79K-$106K (Glassdoor est.)        Raj   Mr.       Webb   \n",
       "224             92   $79K-$106K (Glassdoor est.)        Raj   Mr.       Webb   \n",
       "225             19   $79K-$106K (Glassdoor est.)        Raj   Mr.       Webb   \n",
       "\n",
       "     Minimum_Salary  Maximum_Salary  Average_Salary  \n",
       "5               137             171             154  \n",
       "6               137             171             154  \n",
       "7               137             171             154  \n",
       "8               137             171             154  \n",
       "10               75             131             103  \n",
       "..              ...             ...             ...  \n",
       "220              71             123              97  \n",
       "222              79             106              93  \n",
       "223              79             106              93  \n",
       "224              79             106              93  \n",
       "225              79             106              93  \n",
       "\n",
       "[143 rows x 12 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df=pd.read_csv('Cricket Dataset.csv')\n",
    "df = df.dropna(how= 'all', axis = 0 )# Deleting totally blank rows\n",
    "df = df.dropna(how= 'all', axis = 1 )# Deleting totally blank columns\n",
    "df.rename(columns=df.iloc[0], inplace = True) #Renaming the column names with the 1st row values\n",
    "df.drop(df.index[0], inplace = True) #Dropping the row which is made as column names\n",
    "df.columns = df.columns.str.strip() # stripping spaces from column names and reassigning the clean column names to the dataset\n",
    "df=df.replace(' ', np.NaN) # replacing any spaces with Null values so that we can use fillna method\n",
    "df=df.replace('', np.NaN)\n",
    "df = df.dropna(subset = ['Match Number','Number of Wickets Taken']) # Deleting all rows where Match Number and Number of Wickets Taken are empty. \n",
    "#df = df.fillna(method = 'ffill')  #Forward filling the dataset\n",
    "df=df.ffill()\n",
    "\n",
    "# Splitting of Player Name column into thier First Name, Title and Last Name respectively\n",
    "\n",
    "df['First Name'] = df['Player Name'].str.split().str[0]\n",
    "df['Title'] = df['Player Name'].loc[df['Player Name'].str.split().str.len() == 3].str.split(expand=True)[1]\n",
    "df['Last Name'] = df['Player Name'].str.split().str[-1]\n",
    "df['Title'] = df['Title'].replace(np.nan,'(NA)') # Formatting null values as (NA) as per the required output file\n",
    "\n",
    "# Splitting Salary column with '-' as delimiter and assigning first and second part as minimum and maximum salary respectively\n",
    "\n",
    "df['Minimum_Salary']=df['Salary Estimate'].str.split(pat='-').str[0] \n",
    "df['Maximum_Salary']=df['Salary Estimate'].str.split(pat='-').str[1]\n",
    "\n",
    "# passing the min and max salary columns to df.replace(), specifying each char and it's replacement to obtain the integer values alone\n",
    "\n",
    "cols = ['Minimum_Salary', 'Maximum_Salary']\n",
    "df[cols] = df[cols].replace({'\\$': '','K': '','\\([A-Z][a-z]+\\s\\w+\\.\\)':''}, regex=True)\n",
    "\n",
    "# Finding average of the salary\n",
    "\n",
    "df['Minimum_Salary'] = df['Minimum_Salary'].astype(int)\n",
    "df['Maximum_Salary'] = df['Maximum_Salary'].astype(int)\n",
    "df['Average_Salary']=(df['Minimum_Salary']+ df['Maximum_Salary'])/2\n",
    "df['Average_Salary'] = np.ceil(df['Average_Salary']).astype(int) #Ceiling function maps x value to the least integer greater than or equal x\n",
    "\n",
    "# To export the output to a csv file\n",
    "\n",
    "df.to_csv('Cleaned_file.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">#  Exploratory data analysis and data visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The grand total number of wickets taken by all the players"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grand total number of wickets taken by all the players\n",
      "268\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Grand total number of wickets taken by all the players\n",
    "\n",
    "df['Number of Wickets Taken'] = df['Number of Wickets Taken'].astype(int)\n",
    "print('Grand total number of wickets taken by all the players')\n",
    "sum= df['Number of Wickets Taken'].sum()\n",
    "print(sum)\n",
    "# Graph to show comparison between Match Numbers\n",
    "df['Number of Wickets Taken'] = df['Number of Wickets Taken'].astype(int)\n",
    "df_pivot=pd.pivot_table(df,values=\"Number of Wickets Taken\",index=\"Match Number\",aggfunc='sum')\n",
    "df_pivot.plot(kind='bar',color='purple',edgecolor='green')\n",
    "plt.xlabel('Match Numbers',fontsize=12)\n",
    "plt.ylabel('Total Number of Wickets Taken')\n",
    "plt.title('Total wickets per match')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From the above graph, it is observed that most wickets are taken in Match 1 compared to all other matches."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing the performance of two or more players by plotting their wickets taken and runs scored in the same graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       Number of Wickets Taken  Number of Runs\n",
      "Player Name                                                   \n",
      "Allegra Mrs. Bradford                        5             143\n",
      "Amayah Ms. Baxter                            6             247\n",
      "Ameer Aldred                                 7             187\n",
      "Arda Thornton                                4             130\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "df['Number of Runs'] = df['Number of Runs'].astype(int)\n",
    "grouped = df.groupby('Player Name')\n",
    "grouped.sum(numeric_only=True)\n",
    "df1=grouped[['Number of Wickets Taken','Number of Runs']].sum()\n",
    "df2=df1.loc['Allegra Mrs. Bradford':'Arda Thornton',]\n",
    "sns.scatterplot(x=df2['Number of Runs'], y=df2['Number of Wickets Taken'],hue=df2.index,data=df2['Number of Wickets Taken'])\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### It is observed from the plot that the player named Ameer Aldred has performed the highest and Arda Thornton is the lowest between the 4 players taken for comparison."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating summary statistics (mean, median, standard deviation, etc.) for the number of wickets and runs and Visualizing them using box plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary statistics\n",
      "       Number of Wickets Taken  Number of Runs\n",
      "count               143.000000      143.000000\n",
      "mean                  1.874126       52.559441\n",
      "std                   1.476898       29.110189\n",
      "min                   0.000000        0.000000\n",
      "25%                   0.000000       30.000000\n",
      "50%                   2.000000       54.000000\n",
      "75%                   3.000000       76.000000\n",
      "max                   4.000000      100.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 = df[['Number of Wickets Taken', 'Number of Runs']].describe()\n",
    "print('Summary statistics')\n",
    "print(df2)\n",
    "\n",
    "plt.figure(figsize=(7, 7))\n",
    "df.boxplot(column=['Number of Wickets Taken', 'Number of Runs'])\n",
    "plt.title('Box plots for Wickets and Runs')\n",
    "plt.ylabel('Range')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Is there a correlation between the number of wickets taken and the number of runs scored by a player in a match?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df['Number of Wickets Taken'], df['Number of Runs'], color='green', alpha=0.7,marker='o')\n",
    "plt.title('Scatter Plot of Wickets vs. Runs')\n",
    "plt.xlabel('Number of Wickets Taken')\n",
    "plt.ylabel('Number of Runs')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the above scatter plot, it is observed that there is no correlation between the number of wickets taken and the number of runs scored by a player in a match"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The top players with the highest number of wickets taken or runs scored using a bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 players with highest number of runs scored\n",
      "Player Name\n",
      "Maud Mr. Nicholson     265\n",
      "Amayah Ms. Baxter      247\n",
      "Marlie Miss. Stacey    245\n",
      "Nadine Sawyer          243\n",
      "Raj  Mr. Webb          231\n",
      "Name: Number of Runs, dtype: int64\n",
      "====================================================\n",
      "Top 5 players with highest number of wickets taken\n",
      "Player Name\n",
      "Hoorain Ms. Aguilar      11\n",
      "Lyle Mr. Mullen          10\n",
      "Dorothy Mrs. Guerrero     9\n",
      "Gurleen Edwards           9\n",
      "Nadine Sawyer             9\n",
      "Name: Number of Wickets Taken, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_5_wickets = df1.nlargest(5, 'Number of Wickets Taken')\n",
    "top_5_wickets = top_5_wickets['Number of Wickets Taken']\n",
    "\n",
    "top_5_runs = df1.nlargest(5, 'Number of Runs')\n",
    "top_5_runs=top_5_runs['Number of Runs']\n",
    "\n",
    "# Merge dataframes on the common column 'Category'\n",
    "merged_df = pd.merge(top_5_wickets, top_5_runs, on='Player Name',how='outer')\n",
    "\n",
    "# Plotting\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Set the width of the bars\n",
    "bar_width = 0.5\n",
    "\n",
    "# Set the index for the x-axis ticks\n",
    "index = np.arange(len(merged_df))\n",
    "\n",
    "# Plotting the first set of bars\n",
    "bars1 = ax.bar(index, merged_df['Number of Wickets Taken'], bar_width, label='Number of Wickets Taken',color='green',edgecolor='black')\n",
    "\n",
    "# Plotting the second set of bars next to the first set\n",
    "bars2 = ax.bar(index + bar_width, merged_df['Number of Runs'], bar_width, label='Number of Runs', color='violet',edgecolor='black')\n",
    "\n",
    "# Add labels and title\n",
    "ax.set_xlabel('Player Names')\n",
    "ax.set_ylabel('Values')\n",
    "ax.set_title('Players with highest number of wickets taken or runs scored')\n",
    "ax.set_xticks(index + bar_width / 2)\n",
    "ax.set_xticklabels(merged_df.index, rotation='vertical')\n",
    "ax.legend()\n",
    "print('Top 5 players with highest number of runs scored')\n",
    "print(top_5_runs)\n",
    "print('====================================================')\n",
    "print('Top 5 players with highest number of wickets taken')\n",
    "print(top_5_wickets)\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relationship between the runs scored, average salary of the players across the matches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Answer:    From the line plot, it is clearly observed that Match 1 has most number of high scores as well as most number of players where their average salary is also high compared to all other matches. From the histogram, we could see that there are more than 40 players who are having their salary average ranging between $115-130K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kanmanithamizhanban/anaconda3/lib/python3.11/site-packages/seaborn/axisgrid.py:118: UserWarning: The figure layout has changed to tight\n",
      "  self._figure.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x161a906d0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x300 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a facetgrid for a day\n",
    "g=sns.FacetGrid(df,col=\"Match Number\")\n",
    "#plot scatterplot for total bill and tips for each day\n",
    "g.map(sns.lineplot,\"Number of Runs\",'Average_Salary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([26., 37., 44., 17.,  4., 10.,  0.,  0.,  0.,  5.]),\n",
       " array([ 76. ,  95.6, 115.2, 134.8, 154.4, 174. , 193.6, 213.2, 232.8,\n",
       "        252.4, 272. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['Average_Salary'],color='pink',edgecolor='yellow')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "># Data Merging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player Code</th>\n",
       "      <th>Player Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Role</th>\n",
       "      <th>Batting strike rate</th>\n",
       "      <th>Age</th>\n",
       "      <th>Captain</th>\n",
       "      <th>Bowling economy rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>England</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>110</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A4578</td>\n",
       "      <td>Logan Sir. Thomas</td>\n",
       "      <td>India</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>130</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>X4545</td>\n",
       "      <td>Avaya Ms. Burris</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>105</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44A56</td>\n",
       "      <td>Marlie Miss. Stacey</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>All-rounder</td>\n",
       "      <td>80</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Z4545</td>\n",
       "      <td>Onur Mr. Bateman</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>67</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Player Code          Player Name          Team         Role  \\\n",
       "0      N45234   Maud Mr. Nicholson       England      Batsman   \n",
       "1       A4578    Logan Sir. Thomas         India      Batsman   \n",
       "2       X4545     Avaya Ms. Burris     Australia       Bowler   \n",
       "3       44A56  Marlie Miss. Stacey   New Zealand  All-rounder   \n",
       "4       Z4545     Onur Mr. Bateman  South Africa      Batsman   \n",
       "\n",
       "   Batting strike rate  Age  Captain  Bowling economy rate  \n",
       "0                  110   24        1                   5.2  \n",
       "1                  130   32        1                   4.0  \n",
       "2                  105   35        1                   4.8  \n",
       "3                   80   27        1                   4.0  \n",
       "4                   67   21        1                   3.0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# custom dataset\n",
    "\n",
    "player_info_df=pd.read_excel('Sample.xlsx')\n",
    "player_info_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Left Join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    When merging two datasets, a left join includes all the rows from the left dataframe as well as the matching rows from the right dataframe depending on the provided key(s). If there is no match in the appropriate dataframe, the columns in the resultant dataframe will contain NaN values. In the below example left join gets us the performance detail of each player who has been assigned/played any of the matches along with their profile info like their age, team, role etc.,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player Code</th>\n",
       "      <th>Player Name</th>\n",
       "      <th>Match Number</th>\n",
       "      <th>Number of Wickets Taken</th>\n",
       "      <th>Number of Runs</th>\n",
       "      <th>Salary Estimate</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Title</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Minimum_Salary</th>\n",
       "      <th>Maximum_Salary</th>\n",
       "      <th>Average_Salary</th>\n",
       "      <th>Team</th>\n",
       "      <th>Role</th>\n",
       "      <th>Batting strike rate</th>\n",
       "      <th>Age</th>\n",
       "      <th>Captain</th>\n",
       "      <th>Bowling economy rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M1</td>\n",
       "      <td>2</td>\n",
       "      <td>82</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "      <td>Maud</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Nicholson</td>\n",
       "      <td>137</td>\n",
       "      <td>171</td>\n",
       "      <td>154</td>\n",
       "      <td>England</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>110.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M3</td>\n",
       "      <td>3</td>\n",
       "      <td>71</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "      <td>Maud</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Nicholson</td>\n",
       "      <td>137</td>\n",
       "      <td>171</td>\n",
       "      <td>154</td>\n",
       "      <td>England</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>110.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M5</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "      <td>Maud</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Nicholson</td>\n",
       "      <td>137</td>\n",
       "      <td>171</td>\n",
       "      <td>154</td>\n",
       "      <td>England</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>110.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M6</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "      <td>Maud</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Nicholson</td>\n",
       "      <td>137</td>\n",
       "      <td>171</td>\n",
       "      <td>154</td>\n",
       "      <td>England</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>110.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4578</td>\n",
       "      <td>Logan Sir. Thomas</td>\n",
       "      <td>M1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>$75K-$131K (Glassdoor est.)</td>\n",
       "      <td>Logan</td>\n",
       "      <td>Sir.</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>75</td>\n",
       "      <td>131</td>\n",
       "      <td>103</td>\n",
       "      <td>India</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>130.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>7845522</td>\n",
       "      <td>Tori Knowles</td>\n",
       "      <td>M1</td>\n",
       "      <td>2</td>\n",
       "      <td>54</td>\n",
       "      <td>$71K-$123K (Glassdoor est.)</td>\n",
       "      <td>Tori</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Knowles</td>\n",
       "      <td>71</td>\n",
       "      <td>123</td>\n",
       "      <td>97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>7875M11</td>\n",
       "      <td>Raj  Mr. Webb</td>\n",
       "      <td>M1</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>$79K-$106K (Glassdoor est.)</td>\n",
       "      <td>Raj</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Webb</td>\n",
       "      <td>79</td>\n",
       "      <td>106</td>\n",
       "      <td>93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>7875M11</td>\n",
       "      <td>Raj  Mr. Webb</td>\n",
       "      <td>M2</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>$79K-$106K (Glassdoor est.)</td>\n",
       "      <td>Raj</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Webb</td>\n",
       "      <td>79</td>\n",
       "      <td>106</td>\n",
       "      <td>93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>7875M11</td>\n",
       "      <td>Raj  Mr. Webb</td>\n",
       "      <td>M3</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>$79K-$106K (Glassdoor est.)</td>\n",
       "      <td>Raj</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Webb</td>\n",
       "      <td>79</td>\n",
       "      <td>106</td>\n",
       "      <td>93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>7875M11</td>\n",
       "      <td>Raj  Mr. Webb</td>\n",
       "      <td>M4</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>$79K-$106K (Glassdoor est.)</td>\n",
       "      <td>Raj</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Webb</td>\n",
       "      <td>79</td>\n",
       "      <td>106</td>\n",
       "      <td>93</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Player Code         Player Name Match Number  Number of Wickets Taken  \\\n",
       "0        N45234  Maud Mr. Nicholson           M1                        2   \n",
       "1        N45234  Maud Mr. Nicholson           M3                        3   \n",
       "2        N45234  Maud Mr. Nicholson           M5                        0   \n",
       "3        N45234  Maud Mr. Nicholson           M6                        1   \n",
       "4         A4578   Logan Sir. Thomas           M1                        0   \n",
       "..          ...                 ...          ...                      ...   \n",
       "138     7845522        Tori Knowles           M1                        2   \n",
       "139     7875M11       Raj  Mr. Webb           M1                        1   \n",
       "140     7875M11       Raj  Mr. Webb           M2                        4   \n",
       "141     7875M11       Raj  Mr. Webb           M3                        0   \n",
       "142     7875M11       Raj  Mr. Webb           M4                        1   \n",
       "\n",
       "     Number of Runs               Salary Estimate First Name Title  Last Name  \\\n",
       "0                82  $137K-$171K (Glassdoor est.)       Maud   Mr.  Nicholson   \n",
       "1                71  $137K-$171K (Glassdoor est.)       Maud   Mr.  Nicholson   \n",
       "2                36  $137K-$171K (Glassdoor est.)       Maud   Mr.  Nicholson   \n",
       "3                76  $137K-$171K (Glassdoor est.)       Maud   Mr.  Nicholson   \n",
       "4                 4   $75K-$131K (Glassdoor est.)      Logan  Sir.     Thomas   \n",
       "..              ...                           ...        ...   ...        ...   \n",
       "138              54   $71K-$123K (Glassdoor est.)       Tori  (NA)    Knowles   \n",
       "139              89   $79K-$106K (Glassdoor est.)        Raj   Mr.       Webb   \n",
       "140              31   $79K-$106K (Glassdoor est.)        Raj   Mr.       Webb   \n",
       "141              92   $79K-$106K (Glassdoor est.)        Raj   Mr.       Webb   \n",
       "142              19   $79K-$106K (Glassdoor est.)        Raj   Mr.       Webb   \n",
       "\n",
       "     Minimum_Salary  Maximum_Salary  Average_Salary     Team     Role  \\\n",
       "0               137             171             154  England  Batsman   \n",
       "1               137             171             154  England  Batsman   \n",
       "2               137             171             154  England  Batsman   \n",
       "3               137             171             154  England  Batsman   \n",
       "4                75             131             103    India  Batsman   \n",
       "..              ...             ...             ...      ...      ...   \n",
       "138              71             123              97      NaN      NaN   \n",
       "139              79             106              93      NaN      NaN   \n",
       "140              79             106              93      NaN      NaN   \n",
       "141              79             106              93      NaN      NaN   \n",
       "142              79             106              93      NaN      NaN   \n",
       "\n",
       "     Batting strike rate   Age  Captain  Bowling economy rate  \n",
       "0                  110.0  24.0      1.0                   5.2  \n",
       "1                  110.0  24.0      1.0                   5.2  \n",
       "2                  110.0  24.0      1.0                   5.2  \n",
       "3                  110.0  24.0      1.0                   5.2  \n",
       "4                  130.0  32.0      1.0                   4.0  \n",
       "..                   ...   ...      ...                   ...  \n",
       "138                  NaN   NaN      NaN                   NaN  \n",
       "139                  NaN   NaN      NaN                   NaN  \n",
       "140                  NaN   NaN      NaN                   NaN  \n",
       "141                  NaN   NaN      NaN                   NaN  \n",
       "142                  NaN   NaN      NaN                   NaN  \n",
       "\n",
       "[143 rows x 18 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left_merged=pd.merge(df,player_info_df,on=['Player Code','Player Name'],how='left')\n",
    "left_merged"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Right Join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    1. The primary distinction is whether all rows of a dataframe are included in the result. It is the right dataframe in a right join and the left dataframe in a left join. The choice between right and left join is determined by which dataframe's data you wish to keep entirely in the merged result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    2. Whenever we need to find the players who are not assigned/played any matches but still a part of the team as coaches/substitutes, we can use right join to get the all the rows from player info table with/without their match information from our left table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player Code</th>\n",
       "      <th>Player Name</th>\n",
       "      <th>Match Number</th>\n",
       "      <th>Number of Wickets Taken</th>\n",
       "      <th>Number of Runs</th>\n",
       "      <th>Salary Estimate</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Title</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Minimum_Salary</th>\n",
       "      <th>Maximum_Salary</th>\n",
       "      <th>Average_Salary</th>\n",
       "      <th>Team</th>\n",
       "      <th>Role</th>\n",
       "      <th>Batting strike rate</th>\n",
       "      <th>Age</th>\n",
       "      <th>Captain</th>\n",
       "      <th>Bowling economy rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "      <td>Maud</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Nicholson</td>\n",
       "      <td>137.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>England</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>110</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "      <td>Maud</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Nicholson</td>\n",
       "      <td>137.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>England</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>110</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "      <td>Maud</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Nicholson</td>\n",
       "      <td>137.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>England</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>110</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "      <td>Maud</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Nicholson</td>\n",
       "      <td>137.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>England</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>110</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4578</td>\n",
       "      <td>Logan Sir. Thomas</td>\n",
       "      <td>M1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$75K-$131K (Glassdoor est.)</td>\n",
       "      <td>Logan</td>\n",
       "      <td>Sir.</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>75.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>India</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>130</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>12345</td>\n",
       "      <td>Fannie Murray</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>India</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>115</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>90647</td>\n",
       "      <td>Donovan Firth</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>120</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>977</td>\n",
       "      <td>Kristie Weiss</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Srilanka</td>\n",
       "      <td>All-rounder</td>\n",
       "      <td>80</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>3829</td>\n",
       "      <td>Gurleen Edwards</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>England</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>70</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>E2324</td>\n",
       "      <td>Tyriq Crouch</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>India</td>\n",
       "      <td>All-rounder</td>\n",
       "      <td>90</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Player Code         Player Name Match Number  Number of Wickets Taken  \\\n",
       "0       N45234  Maud Mr. Nicholson           M1                      2.0   \n",
       "1       N45234  Maud Mr. Nicholson           M3                      3.0   \n",
       "2       N45234  Maud Mr. Nicholson           M5                      0.0   \n",
       "3       N45234  Maud Mr. Nicholson           M6                      1.0   \n",
       "4        A4578   Logan Sir. Thomas           M1                      0.0   \n",
       "..         ...                 ...          ...                      ...   \n",
       "57       12345       Fannie Murray          NaN                      NaN   \n",
       "58       90647       Donovan Firth          NaN                      NaN   \n",
       "59         977       Kristie Weiss          NaN                      NaN   \n",
       "60        3829     Gurleen Edwards          NaN                      NaN   \n",
       "61       E2324        Tyriq Crouch          NaN                      NaN   \n",
       "\n",
       "    Number of Runs               Salary Estimate First Name Title  Last Name  \\\n",
       "0             82.0  $137K-$171K (Glassdoor est.)       Maud   Mr.  Nicholson   \n",
       "1             71.0  $137K-$171K (Glassdoor est.)       Maud   Mr.  Nicholson   \n",
       "2             36.0  $137K-$171K (Glassdoor est.)       Maud   Mr.  Nicholson   \n",
       "3             76.0  $137K-$171K (Glassdoor est.)       Maud   Mr.  Nicholson   \n",
       "4              4.0   $75K-$131K (Glassdoor est.)      Logan  Sir.     Thomas   \n",
       "..             ...                           ...        ...   ...        ...   \n",
       "57             NaN                           NaN        NaN   NaN        NaN   \n",
       "58             NaN                           NaN        NaN   NaN        NaN   \n",
       "59             NaN                           NaN        NaN   NaN        NaN   \n",
       "60             NaN                           NaN        NaN   NaN        NaN   \n",
       "61             NaN                           NaN        NaN   NaN        NaN   \n",
       "\n",
       "    Minimum_Salary  Maximum_Salary  Average_Salary      Team         Role  \\\n",
       "0            137.0           171.0           154.0   England      Batsman   \n",
       "1            137.0           171.0           154.0   England      Batsman   \n",
       "2            137.0           171.0           154.0   England      Batsman   \n",
       "3            137.0           171.0           154.0   England      Batsman   \n",
       "4             75.0           131.0           103.0     India      Batsman   \n",
       "..             ...             ...             ...       ...          ...   \n",
       "57             NaN             NaN             NaN     India      Batsman   \n",
       "58             NaN             NaN             NaN  Pakistan       Bowler   \n",
       "59             NaN             NaN             NaN  Srilanka  All-rounder   \n",
       "60             NaN             NaN             NaN   England       Bowler   \n",
       "61             NaN             NaN             NaN     India  All-rounder   \n",
       "\n",
       "    Batting strike rate  Age  Captain  Bowling economy rate  \n",
       "0                   110   24        1                   5.2  \n",
       "1                   110   24        1                   5.2  \n",
       "2                   110   24        1                   5.2  \n",
       "3                   110   24        1                   5.2  \n",
       "4                   130   32        1                   4.0  \n",
       "..                  ...  ...      ...                   ...  \n",
       "57                  115   26        0                   4.0  \n",
       "58                  120   31        0                   4.8  \n",
       "59                   80   35        0                   4.0  \n",
       "60                   70   23        0                   3.0  \n",
       "61                   90   27        0                   2.5  \n",
       "\n",
       "[62 rows x 18 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right_merged=pd.merge(df,player_info_df,on=['Player Code','Player Name'],how='right')\n",
    "right_merged"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outer Join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    1. An outside join is often used in data merging when you wish to incorporate all rows from both datasets, regardless of whether there is a match based on the provided key(s). It is useful in situations where you want to build a merged dataset that keeps information from both dataframes, filling in missing values with NaN for dataframe columns where no match is found."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    2. We can use outer join to find the average of batting strike rate or bowling economy rate average or average salary of overall team members. For example,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player Code</th>\n",
       "      <th>Player Name</th>\n",
       "      <th>Match Number</th>\n",
       "      <th>Number of Wickets Taken</th>\n",
       "      <th>Number of Runs</th>\n",
       "      <th>Salary Estimate</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Title</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Minimum_Salary</th>\n",
       "      <th>Maximum_Salary</th>\n",
       "      <th>Average_Salary</th>\n",
       "      <th>Team</th>\n",
       "      <th>Role</th>\n",
       "      <th>Batting strike rate</th>\n",
       "      <th>Age</th>\n",
       "      <th>Captain</th>\n",
       "      <th>Bowling economy rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "      <td>Maud</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Nicholson</td>\n",
       "      <td>137.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>England</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>110.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "      <td>Maud</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Nicholson</td>\n",
       "      <td>137.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>England</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>110.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "      <td>Maud</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Nicholson</td>\n",
       "      <td>137.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>England</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>110.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "      <td>Maud</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Nicholson</td>\n",
       "      <td>137.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>England</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>110.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4578</td>\n",
       "      <td>Logan Sir. Thomas</td>\n",
       "      <td>M1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>$75K-$131K (Glassdoor est.)</td>\n",
       "      <td>Logan</td>\n",
       "      <td>Sir.</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>75.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>India</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>130.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>12345</td>\n",
       "      <td>Fannie Murray</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>India</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>115.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>90647</td>\n",
       "      <td>Donovan Firth</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>120.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>977</td>\n",
       "      <td>Kristie Weiss</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Srilanka</td>\n",
       "      <td>All-rounder</td>\n",
       "      <td>80.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>3829</td>\n",
       "      <td>Gurleen Edwards</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>England</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>70.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>E2324</td>\n",
       "      <td>Tyriq Crouch</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>India</td>\n",
       "      <td>All-rounder</td>\n",
       "      <td>90.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Player Code         Player Name Match Number  Number of Wickets Taken  \\\n",
       "0        N45234  Maud Mr. Nicholson           M1                      2.0   \n",
       "1        N45234  Maud Mr. Nicholson           M3                      3.0   \n",
       "2        N45234  Maud Mr. Nicholson           M5                      0.0   \n",
       "3        N45234  Maud Mr. Nicholson           M6                      1.0   \n",
       "4         A4578   Logan Sir. Thomas           M1                      0.0   \n",
       "..          ...                 ...          ...                      ...   \n",
       "145       12345       Fannie Murray          NaN                      NaN   \n",
       "146       90647       Donovan Firth          NaN                      NaN   \n",
       "147         977       Kristie Weiss          NaN                      NaN   \n",
       "148        3829     Gurleen Edwards          NaN                      NaN   \n",
       "149       E2324        Tyriq Crouch          NaN                      NaN   \n",
       "\n",
       "     Number of Runs               Salary Estimate First Name Title  Last Name  \\\n",
       "0              82.0  $137K-$171K (Glassdoor est.)       Maud   Mr.  Nicholson   \n",
       "1              71.0  $137K-$171K (Glassdoor est.)       Maud   Mr.  Nicholson   \n",
       "2              36.0  $137K-$171K (Glassdoor est.)       Maud   Mr.  Nicholson   \n",
       "3              76.0  $137K-$171K (Glassdoor est.)       Maud   Mr.  Nicholson   \n",
       "4               4.0   $75K-$131K (Glassdoor est.)      Logan  Sir.     Thomas   \n",
       "..              ...                           ...        ...   ...        ...   \n",
       "145             NaN                           NaN        NaN   NaN        NaN   \n",
       "146             NaN                           NaN        NaN   NaN        NaN   \n",
       "147             NaN                           NaN        NaN   NaN        NaN   \n",
       "148             NaN                           NaN        NaN   NaN        NaN   \n",
       "149             NaN                           NaN        NaN   NaN        NaN   \n",
       "\n",
       "     Minimum_Salary  Maximum_Salary  Average_Salary      Team         Role  \\\n",
       "0             137.0           171.0           154.0   England      Batsman   \n",
       "1             137.0           171.0           154.0   England      Batsman   \n",
       "2             137.0           171.0           154.0   England      Batsman   \n",
       "3             137.0           171.0           154.0   England      Batsman   \n",
       "4              75.0           131.0           103.0     India      Batsman   \n",
       "..              ...             ...             ...       ...          ...   \n",
       "145             NaN             NaN             NaN     India      Batsman   \n",
       "146             NaN             NaN             NaN  Pakistan       Bowler   \n",
       "147             NaN             NaN             NaN  Srilanka  All-rounder   \n",
       "148             NaN             NaN             NaN   England       Bowler   \n",
       "149             NaN             NaN             NaN     India  All-rounder   \n",
       "\n",
       "     Batting strike rate   Age  Captain  Bowling economy rate  \n",
       "0                  110.0  24.0      1.0                   5.2  \n",
       "1                  110.0  24.0      1.0                   5.2  \n",
       "2                  110.0  24.0      1.0                   5.2  \n",
       "3                  110.0  24.0      1.0                   5.2  \n",
       "4                  130.0  32.0      1.0                   4.0  \n",
       "..                   ...   ...      ...                   ...  \n",
       "145                115.0  26.0      0.0                   4.0  \n",
       "146                120.0  31.0      0.0                   4.8  \n",
       "147                 80.0  35.0      0.0                   4.0  \n",
       "148                 70.0  23.0      0.0                   3.0  \n",
       "149                 90.0  27.0      0.0                   2.5  \n",
       "\n",
       "[150 rows x 18 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outer_merged=pd.merge(df,player_info_df,on=['Player Code','Player Name'],how='outer')\n",
    "outer_merged"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inner Join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    1. An inner merge information on these data sets can help us to identify each player's age, which provides insight into the age distribution of players in relation to their performance and salary. We may also examine how different player positions (batsman, bowler, all-rounder), batting strike rates, and bowling economy rates match, as well as providing insights into their performance metrics, pay, and other facets of the game."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    2. \n",
    "    a.) The Difficulty of Analyzing Outliers:  Outliers or exceptional cases in one table that are not present in the other will be excluded from the analysis. This could be a problem if you're looking for outliers or unusual cases.\n",
    "    b.) Identifying Data Quality Issues is Difficult:  An inner join may obscure concerns with data quality, such as missing or inconsistent key values, making it more difficult to discover and address these issues in the combined dataset.\n",
    "    c.) Data loss: The most major disadvantage of an inner join is that it rejects rows that do not have matching keys in both tables. Players who appear in either Table 1 or Table 2 but not both will be eliminated from the results. This could result in the loss of crucial information, especially if you want to analyze all participants, regardless of whether they have complete information in both tables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player Code</th>\n",
       "      <th>Player Name</th>\n",
       "      <th>Match Number</th>\n",
       "      <th>Number of Wickets Taken</th>\n",
       "      <th>Number of Runs</th>\n",
       "      <th>Salary Estimate</th>\n",
       "      <th>First Name</th>\n",
       "      <th>Title</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Minimum_Salary</th>\n",
       "      <th>Maximum_Salary</th>\n",
       "      <th>Average_Salary</th>\n",
       "      <th>Team</th>\n",
       "      <th>Role</th>\n",
       "      <th>Batting strike rate</th>\n",
       "      <th>Age</th>\n",
       "      <th>Captain</th>\n",
       "      <th>Bowling economy rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M1</td>\n",
       "      <td>2</td>\n",
       "      <td>82</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "      <td>Maud</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Nicholson</td>\n",
       "      <td>137</td>\n",
       "      <td>171</td>\n",
       "      <td>154</td>\n",
       "      <td>England</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>110</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M3</td>\n",
       "      <td>3</td>\n",
       "      <td>71</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "      <td>Maud</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Nicholson</td>\n",
       "      <td>137</td>\n",
       "      <td>171</td>\n",
       "      <td>154</td>\n",
       "      <td>England</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>110</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M5</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "      <td>Maud</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Nicholson</td>\n",
       "      <td>137</td>\n",
       "      <td>171</td>\n",
       "      <td>154</td>\n",
       "      <td>England</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>110</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>N45234</td>\n",
       "      <td>Maud Mr. Nicholson</td>\n",
       "      <td>M6</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>$137K-$171K (Glassdoor est.)</td>\n",
       "      <td>Maud</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Nicholson</td>\n",
       "      <td>137</td>\n",
       "      <td>171</td>\n",
       "      <td>154</td>\n",
       "      <td>England</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>110</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4578</td>\n",
       "      <td>Logan Sir. Thomas</td>\n",
       "      <td>M1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>$75K-$131K (Glassdoor est.)</td>\n",
       "      <td>Logan</td>\n",
       "      <td>Sir.</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>75</td>\n",
       "      <td>131</td>\n",
       "      <td>103</td>\n",
       "      <td>India</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>130</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A4578</td>\n",
       "      <td>Logan Sir. Thomas</td>\n",
       "      <td>M3</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>$75K-$131K (Glassdoor est.)</td>\n",
       "      <td>Logan</td>\n",
       "      <td>Sir.</td>\n",
       "      <td>Thomas</td>\n",
       "      <td>75</td>\n",
       "      <td>131</td>\n",
       "      <td>103</td>\n",
       "      <td>India</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>130</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>X4545</td>\n",
       "      <td>Avaya Ms. Burris</td>\n",
       "      <td>M1</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>$100K-$131K (Glassdoor est.)</td>\n",
       "      <td>Avaya</td>\n",
       "      <td>Ms.</td>\n",
       "      <td>Burris</td>\n",
       "      <td>100</td>\n",
       "      <td>131</td>\n",
       "      <td>116</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>105</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>X4545</td>\n",
       "      <td>Avaya Ms. Burris</td>\n",
       "      <td>M3</td>\n",
       "      <td>0</td>\n",
       "      <td>76</td>\n",
       "      <td>$100K-$131K (Glassdoor est.)</td>\n",
       "      <td>Avaya</td>\n",
       "      <td>Ms.</td>\n",
       "      <td>Burris</td>\n",
       "      <td>100</td>\n",
       "      <td>131</td>\n",
       "      <td>116</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>105</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>X4545</td>\n",
       "      <td>Avaya Ms. Burris</td>\n",
       "      <td>M5</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>$100K-$131K (Glassdoor est.)</td>\n",
       "      <td>Avaya</td>\n",
       "      <td>Ms.</td>\n",
       "      <td>Burris</td>\n",
       "      <td>100</td>\n",
       "      <td>131</td>\n",
       "      <td>116</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>105</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>44A56</td>\n",
       "      <td>Marlie Miss. Stacey</td>\n",
       "      <td>M1</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>$50K-$101K (Glassdoor est.)</td>\n",
       "      <td>Marlie</td>\n",
       "      <td>Miss.</td>\n",
       "      <td>Stacey</td>\n",
       "      <td>50</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>All-rounder</td>\n",
       "      <td>80</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>44A56</td>\n",
       "      <td>Marlie Miss. Stacey</td>\n",
       "      <td>M3</td>\n",
       "      <td>4</td>\n",
       "      <td>58</td>\n",
       "      <td>$50K-$101K (Glassdoor est.)</td>\n",
       "      <td>Marlie</td>\n",
       "      <td>Miss.</td>\n",
       "      <td>Stacey</td>\n",
       "      <td>50</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>All-rounder</td>\n",
       "      <td>80</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>44A56</td>\n",
       "      <td>Marlie Miss. Stacey</td>\n",
       "      <td>M5</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>$50K-$101K (Glassdoor est.)</td>\n",
       "      <td>Marlie</td>\n",
       "      <td>Miss.</td>\n",
       "      <td>Stacey</td>\n",
       "      <td>50</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>All-rounder</td>\n",
       "      <td>80</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Z4545</td>\n",
       "      <td>Onur Mr. Bateman</td>\n",
       "      <td>M1</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>$101K-$165K (Glassdoor est.)</td>\n",
       "      <td>Onur</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Bateman</td>\n",
       "      <td>101</td>\n",
       "      <td>165</td>\n",
       "      <td>133</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>67</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4447X</td>\n",
       "      <td>Lyle Mr. Mullen</td>\n",
       "      <td>M1</td>\n",
       "      <td>4</td>\n",
       "      <td>58</td>\n",
       "      <td>$101K-$165K (Glassdoor est.)</td>\n",
       "      <td>Lyle</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Mullen</td>\n",
       "      <td>101</td>\n",
       "      <td>165</td>\n",
       "      <td>133</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>95</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4447X</td>\n",
       "      <td>Lyle Mr. Mullen</td>\n",
       "      <td>M3</td>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>$101K-$165K (Glassdoor est.)</td>\n",
       "      <td>Lyle</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Mullen</td>\n",
       "      <td>101</td>\n",
       "      <td>165</td>\n",
       "      <td>133</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>95</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4447X</td>\n",
       "      <td>Lyle Mr. Mullen</td>\n",
       "      <td>M5</td>\n",
       "      <td>2</td>\n",
       "      <td>98</td>\n",
       "      <td>$101K-$165K (Glassdoor est.)</td>\n",
       "      <td>Lyle</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Mullen</td>\n",
       "      <td>101</td>\n",
       "      <td>165</td>\n",
       "      <td>133</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>95</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2#3256</td>\n",
       "      <td>Lea Mrs. Driscoll</td>\n",
       "      <td>M1</td>\n",
       "      <td>3</td>\n",
       "      <td>72</td>\n",
       "      <td>$101K-$165K (Glassdoor est.)</td>\n",
       "      <td>Lea</td>\n",
       "      <td>Mrs.</td>\n",
       "      <td>Driscoll</td>\n",
       "      <td>101</td>\n",
       "      <td>165</td>\n",
       "      <td>133</td>\n",
       "      <td>Srilanka</td>\n",
       "      <td>All-rounder</td>\n",
       "      <td>200</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Y4564</td>\n",
       "      <td>Kaylee Mrs. Pitt</td>\n",
       "      <td>M1</td>\n",
       "      <td>2</td>\n",
       "      <td>58</td>\n",
       "      <td>$101K-$165K (Glassdoor est.)</td>\n",
       "      <td>Kaylee</td>\n",
       "      <td>Mrs.</td>\n",
       "      <td>Pitt</td>\n",
       "      <td>101</td>\n",
       "      <td>165</td>\n",
       "      <td>133</td>\n",
       "      <td>England</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>170</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Y4564</td>\n",
       "      <td>Kaylee Mrs. Pitt</td>\n",
       "      <td>M3</td>\n",
       "      <td>2</td>\n",
       "      <td>82</td>\n",
       "      <td>$101K-$165K (Glassdoor est.)</td>\n",
       "      <td>Kaylee</td>\n",
       "      <td>Mrs.</td>\n",
       "      <td>Pitt</td>\n",
       "      <td>101</td>\n",
       "      <td>165</td>\n",
       "      <td>133</td>\n",
       "      <td>England</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>170</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Y4564</td>\n",
       "      <td>Kaylee Mrs. Pitt</td>\n",
       "      <td>M5</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "      <td>$101K-$165K (Glassdoor est.)</td>\n",
       "      <td>Kaylee</td>\n",
       "      <td>Mrs.</td>\n",
       "      <td>Pitt</td>\n",
       "      <td>101</td>\n",
       "      <td>165</td>\n",
       "      <td>133</td>\n",
       "      <td>England</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>170</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>O7456</td>\n",
       "      <td>Amayah Ms. Baxter</td>\n",
       "      <td>M1</td>\n",
       "      <td>2</td>\n",
       "      <td>33</td>\n",
       "      <td>$101K-$165K (Glassdoor est.)</td>\n",
       "      <td>Amayah</td>\n",
       "      <td>Ms.</td>\n",
       "      <td>Baxter</td>\n",
       "      <td>101</td>\n",
       "      <td>165</td>\n",
       "      <td>133</td>\n",
       "      <td>India</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>145</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>O7456</td>\n",
       "      <td>Amayah Ms. Baxter</td>\n",
       "      <td>M3</td>\n",
       "      <td>0</td>\n",
       "      <td>94</td>\n",
       "      <td>$101K-$165K (Glassdoor est.)</td>\n",
       "      <td>Amayah</td>\n",
       "      <td>Ms.</td>\n",
       "      <td>Baxter</td>\n",
       "      <td>101</td>\n",
       "      <td>165</td>\n",
       "      <td>133</td>\n",
       "      <td>India</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>145</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>O7456</td>\n",
       "      <td>Amayah Ms. Baxter</td>\n",
       "      <td>M5</td>\n",
       "      <td>4</td>\n",
       "      <td>89</td>\n",
       "      <td>$101K-$165K (Glassdoor est.)</td>\n",
       "      <td>Amayah</td>\n",
       "      <td>Ms.</td>\n",
       "      <td>Baxter</td>\n",
       "      <td>101</td>\n",
       "      <td>165</td>\n",
       "      <td>133</td>\n",
       "      <td>India</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>145</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>O7456</td>\n",
       "      <td>Amayah Ms. Baxter</td>\n",
       "      <td>M8</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>$101K-$165K (Glassdoor est.)</td>\n",
       "      <td>Amayah</td>\n",
       "      <td>Ms.</td>\n",
       "      <td>Baxter</td>\n",
       "      <td>101</td>\n",
       "      <td>165</td>\n",
       "      <td>133</td>\n",
       "      <td>India</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>145</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>X45645</td>\n",
       "      <td>Dorothy Mrs. Guerrero</td>\n",
       "      <td>M1</td>\n",
       "      <td>3</td>\n",
       "      <td>62</td>\n",
       "      <td>$101K-$165K (Glassdoor est.)</td>\n",
       "      <td>Dorothy</td>\n",
       "      <td>Mrs.</td>\n",
       "      <td>Guerrero</td>\n",
       "      <td>101</td>\n",
       "      <td>165</td>\n",
       "      <td>133</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>120</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>X45645</td>\n",
       "      <td>Dorothy Mrs. Guerrero</td>\n",
       "      <td>M3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>$101K-$165K (Glassdoor est.)</td>\n",
       "      <td>Dorothy</td>\n",
       "      <td>Mrs.</td>\n",
       "      <td>Guerrero</td>\n",
       "      <td>101</td>\n",
       "      <td>165</td>\n",
       "      <td>133</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>120</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>X45645</td>\n",
       "      <td>Dorothy Mrs. Guerrero</td>\n",
       "      <td>M5</td>\n",
       "      <td>3</td>\n",
       "      <td>91</td>\n",
       "      <td>$101K-$165K (Glassdoor est.)</td>\n",
       "      <td>Dorothy</td>\n",
       "      <td>Mrs.</td>\n",
       "      <td>Guerrero</td>\n",
       "      <td>101</td>\n",
       "      <td>165</td>\n",
       "      <td>133</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>120</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>X45645</td>\n",
       "      <td>Dorothy Mrs. Guerrero</td>\n",
       "      <td>M7</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "      <td>$101K-$165K (Glassdoor est.)</td>\n",
       "      <td>Dorothy</td>\n",
       "      <td>Mrs.</td>\n",
       "      <td>Guerrero</td>\n",
       "      <td>101</td>\n",
       "      <td>165</td>\n",
       "      <td>133</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>120</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>P74854</td>\n",
       "      <td>Misty Dunne</td>\n",
       "      <td>M1</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>$101K-$165K (Glassdoor est.)</td>\n",
       "      <td>Misty</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Dunne</td>\n",
       "      <td>101</td>\n",
       "      <td>165</td>\n",
       "      <td>133</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>100</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>P74854</td>\n",
       "      <td>Misty Dunne</td>\n",
       "      <td>M3</td>\n",
       "      <td>3</td>\n",
       "      <td>57</td>\n",
       "      <td>$101K-$165K (Glassdoor est.)</td>\n",
       "      <td>Misty</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Dunne</td>\n",
       "      <td>101</td>\n",
       "      <td>165</td>\n",
       "      <td>133</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>100</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>P74854</td>\n",
       "      <td>Misty Dunne</td>\n",
       "      <td>M5</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>$101K-$165K (Glassdoor est.)</td>\n",
       "      <td>Misty</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Dunne</td>\n",
       "      <td>101</td>\n",
       "      <td>165</td>\n",
       "      <td>133</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>100</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>564</td>\n",
       "      <td>Mikael Sutherland</td>\n",
       "      <td>M1</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>$122K-$146K (Glassdoor est.)</td>\n",
       "      <td>Mikael</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Sutherland</td>\n",
       "      <td>122</td>\n",
       "      <td>146</td>\n",
       "      <td>134</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>All-rounder</td>\n",
       "      <td>105</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>564</td>\n",
       "      <td>Mikael Sutherland</td>\n",
       "      <td>M2</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>$122K-$146K (Glassdoor est.)</td>\n",
       "      <td>Mikael</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Sutherland</td>\n",
       "      <td>122</td>\n",
       "      <td>146</td>\n",
       "      <td>134</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>All-rounder</td>\n",
       "      <td>105</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>564</td>\n",
       "      <td>Mikael Sutherland</td>\n",
       "      <td>M3</td>\n",
       "      <td>0</td>\n",
       "      <td>47</td>\n",
       "      <td>$122K-$146K (Glassdoor est.)</td>\n",
       "      <td>Mikael</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Sutherland</td>\n",
       "      <td>122</td>\n",
       "      <td>146</td>\n",
       "      <td>134</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>All-rounder</td>\n",
       "      <td>105</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>7878</td>\n",
       "      <td>Nathaniel Gaines</td>\n",
       "      <td>M2</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>$138K-$158K (Glassdoor est.)</td>\n",
       "      <td>Nathaniel</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Gaines</td>\n",
       "      <td>138</td>\n",
       "      <td>158</td>\n",
       "      <td>148</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>110</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>14A54</td>\n",
       "      <td>Ameer Aldred</td>\n",
       "      <td>M1</td>\n",
       "      <td>2</td>\n",
       "      <td>57</td>\n",
       "      <td>$145K-$225K(Employer est.)</td>\n",
       "      <td>Ameer</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Aldred</td>\n",
       "      <td>145</td>\n",
       "      <td>225</td>\n",
       "      <td>185</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>115</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>14A54</td>\n",
       "      <td>Ameer Aldred</td>\n",
       "      <td>M2</td>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>$145K-$225K(Employer est.)</td>\n",
       "      <td>Ameer</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Aldred</td>\n",
       "      <td>145</td>\n",
       "      <td>225</td>\n",
       "      <td>185</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>115</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>14A54</td>\n",
       "      <td>Ameer Aldred</td>\n",
       "      <td>M3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>$145K-$225K(Employer est.)</td>\n",
       "      <td>Ameer</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Aldred</td>\n",
       "      <td>145</td>\n",
       "      <td>225</td>\n",
       "      <td>185</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>115</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>14A54</td>\n",
       "      <td>Ameer Aldred</td>\n",
       "      <td>M5</td>\n",
       "      <td>0</td>\n",
       "      <td>92</td>\n",
       "      <td>$145K-$225K(Employer est.)</td>\n",
       "      <td>Ameer</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Aldred</td>\n",
       "      <td>145</td>\n",
       "      <td>225</td>\n",
       "      <td>185</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>115</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>4541</td>\n",
       "      <td>Eadie Mcdaniel</td>\n",
       "      <td>M1</td>\n",
       "      <td>1</td>\n",
       "      <td>61</td>\n",
       "      <td>$69K-$116K (Glassdoor est.)</td>\n",
       "      <td>Eadie</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Mcdaniel</td>\n",
       "      <td>69</td>\n",
       "      <td>116</td>\n",
       "      <td>93</td>\n",
       "      <td>England</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>120</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>4541</td>\n",
       "      <td>Eadie Mcdaniel</td>\n",
       "      <td>M3</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>$69K-$116K (Glassdoor est.)</td>\n",
       "      <td>Eadie</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Mcdaniel</td>\n",
       "      <td>69</td>\n",
       "      <td>116</td>\n",
       "      <td>93</td>\n",
       "      <td>England</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>120</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4556</td>\n",
       "      <td>Shoaib Busby</td>\n",
       "      <td>M1</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>$79K-$106K (Glassdoor est.)</td>\n",
       "      <td>Shoaib</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Busby</td>\n",
       "      <td>79</td>\n",
       "      <td>106</td>\n",
       "      <td>93</td>\n",
       "      <td>India</td>\n",
       "      <td>All-rounder</td>\n",
       "      <td>80</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>41451</td>\n",
       "      <td>Sadiyah Whitaker</td>\n",
       "      <td>M1</td>\n",
       "      <td>1</td>\n",
       "      <td>74</td>\n",
       "      <td>$79K-$133K (Glassdoor est.)</td>\n",
       "      <td>Sadiyah</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Whitaker</td>\n",
       "      <td>79</td>\n",
       "      <td>133</td>\n",
       "      <td>106</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>70</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>444545</td>\n",
       "      <td>Haidar Mccormick</td>\n",
       "      <td>M1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>$92K-$155K (Glassdoor est.)</td>\n",
       "      <td>Haidar</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Mccormick</td>\n",
       "      <td>92</td>\n",
       "      <td>155</td>\n",
       "      <td>124</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>All-rounder</td>\n",
       "      <td>75</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>444545</td>\n",
       "      <td>Haidar Mccormick</td>\n",
       "      <td>M3</td>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "      <td>$92K-$155K (Glassdoor est.)</td>\n",
       "      <td>Haidar</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Mccormick</td>\n",
       "      <td>92</td>\n",
       "      <td>155</td>\n",
       "      <td>124</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>All-rounder</td>\n",
       "      <td>75</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>444545</td>\n",
       "      <td>Haidar Mccormick</td>\n",
       "      <td>M5</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>$92K-$155K (Glassdoor est.)</td>\n",
       "      <td>Haidar</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Mccormick</td>\n",
       "      <td>92</td>\n",
       "      <td>155</td>\n",
       "      <td>124</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>All-rounder</td>\n",
       "      <td>75</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>4212121</td>\n",
       "      <td>Jarrad Ratliff</td>\n",
       "      <td>M1</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>$101K-$165K (Glassdoor est.)</td>\n",
       "      <td>Jarrad</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Ratliff</td>\n",
       "      <td>101</td>\n",
       "      <td>165</td>\n",
       "      <td>133</td>\n",
       "      <td>Srilanka</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>135</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>7486748</td>\n",
       "      <td>Jade Dean</td>\n",
       "      <td>M1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>$110K-$163K (Glassdoor est.)</td>\n",
       "      <td>Jade</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Dean</td>\n",
       "      <td>110</td>\n",
       "      <td>163</td>\n",
       "      <td>137</td>\n",
       "      <td>Srilanks</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>175</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>7486748</td>\n",
       "      <td>Jade Dean</td>\n",
       "      <td>M3</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>$110K-$163K (Glassdoor est.)</td>\n",
       "      <td>Jade</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Dean</td>\n",
       "      <td>110</td>\n",
       "      <td>163</td>\n",
       "      <td>137</td>\n",
       "      <td>Srilanks</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>175</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>7486748</td>\n",
       "      <td>Jade Dean</td>\n",
       "      <td>M5</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>$110K-$163K (Glassdoor est.)</td>\n",
       "      <td>Jade</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Dean</td>\n",
       "      <td>110</td>\n",
       "      <td>163</td>\n",
       "      <td>137</td>\n",
       "      <td>Srilanks</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>175</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>4245</td>\n",
       "      <td>Pauline Norton</td>\n",
       "      <td>M1</td>\n",
       "      <td>2</td>\n",
       "      <td>77</td>\n",
       "      <td>$128K-$201K (Glassdoor est.)</td>\n",
       "      <td>Pauline</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Norton</td>\n",
       "      <td>128</td>\n",
       "      <td>201</td>\n",
       "      <td>165</td>\n",
       "      <td>India</td>\n",
       "      <td>Batsman</td>\n",
       "      <td>180</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>7795</td>\n",
       "      <td>Euan Mcguire</td>\n",
       "      <td>M1</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>$138K-$158K (Glassdoor est.)</td>\n",
       "      <td>Euan</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Mcguire</td>\n",
       "      <td>138</td>\n",
       "      <td>158</td>\n",
       "      <td>148</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>105</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>7795</td>\n",
       "      <td>Euan Mcguire</td>\n",
       "      <td>M3</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>$138K-$158K (Glassdoor est.)</td>\n",
       "      <td>Euan</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Mcguire</td>\n",
       "      <td>138</td>\n",
       "      <td>158</td>\n",
       "      <td>148</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Bowler</td>\n",
       "      <td>105</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>122</td>\n",
       "      <td>May Wilkerson</td>\n",
       "      <td>M1</td>\n",
       "      <td>3</td>\n",
       "      <td>89</td>\n",
       "      <td>$212K-$331K (Glassdoor est.)</td>\n",
       "      <td>May</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Wilkerson</td>\n",
       "      <td>212</td>\n",
       "      <td>331</td>\n",
       "      <td>272</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>All-rounder</td>\n",
       "      <td>110</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>122</td>\n",
       "      <td>May Wilkerson</td>\n",
       "      <td>M3</td>\n",
       "      <td>4</td>\n",
       "      <td>54</td>\n",
       "      <td>$212K-$331K (Glassdoor est.)</td>\n",
       "      <td>May</td>\n",
       "      <td>(NA)</td>\n",
       "      <td>Wilkerson</td>\n",
       "      <td>212</td>\n",
       "      <td>331</td>\n",
       "      <td>272</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>All-rounder</td>\n",
       "      <td>110</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>5.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Player Code            Player Name Match Number  Number of Wickets Taken  \\\n",
       "0       N45234     Maud Mr. Nicholson           M1                        2   \n",
       "1       N45234     Maud Mr. Nicholson           M3                        3   \n",
       "2       N45234     Maud Mr. Nicholson           M5                        0   \n",
       "3       N45234     Maud Mr. Nicholson           M6                        1   \n",
       "4        A4578      Logan Sir. Thomas           M1                        0   \n",
       "5        A4578      Logan Sir. Thomas           M3                        1   \n",
       "6        X4545       Avaya Ms. Burris           M1                        0   \n",
       "7        X4545       Avaya Ms. Burris           M3                        0   \n",
       "8        X4545       Avaya Ms. Burris           M5                        0   \n",
       "9        44A56    Marlie Miss. Stacey           M1                        0   \n",
       "10       44A56    Marlie Miss. Stacey           M3                        4   \n",
       "11       44A56    Marlie Miss. Stacey           M5                        0   \n",
       "12       Z4545       Onur Mr. Bateman           M1                        0   \n",
       "13       4447X        Lyle Mr. Mullen           M1                        4   \n",
       "14       4447X        Lyle Mr. Mullen           M3                        4   \n",
       "15       4447X        Lyle Mr. Mullen           M5                        2   \n",
       "16      2#3256      Lea Mrs. Driscoll           M1                        3   \n",
       "17       Y4564       Kaylee Mrs. Pitt           M1                        2   \n",
       "18       Y4564       Kaylee Mrs. Pitt           M3                        2   \n",
       "19       Y4564       Kaylee Mrs. Pitt           M5                        4   \n",
       "20       O7456      Amayah Ms. Baxter           M1                        2   \n",
       "21       O7456      Amayah Ms. Baxter           M3                        0   \n",
       "22       O7456      Amayah Ms. Baxter           M5                        4   \n",
       "23       O7456      Amayah Ms. Baxter           M8                        0   \n",
       "24      X45645  Dorothy Mrs. Guerrero           M1                        3   \n",
       "25      X45645  Dorothy Mrs. Guerrero           M3                        1   \n",
       "26      X45645  Dorothy Mrs. Guerrero           M5                        3   \n",
       "27      X45645  Dorothy Mrs. Guerrero           M7                        2   \n",
       "28      P74854            Misty Dunne           M1                        4   \n",
       "29      P74854            Misty Dunne           M3                        3   \n",
       "30      P74854            Misty Dunne           M5                        0   \n",
       "31         564      Mikael Sutherland           M1                        0   \n",
       "32         564      Mikael Sutherland           M2                        2   \n",
       "33         564      Mikael Sutherland           M3                        0   \n",
       "34        7878       Nathaniel Gaines           M2                        2   \n",
       "35       14A54           Ameer Aldred           M1                        2   \n",
       "36       14A54           Ameer Aldred           M2                        3   \n",
       "37       14A54           Ameer Aldred           M3                        2   \n",
       "38       14A54           Ameer Aldred           M5                        0   \n",
       "39        4541         Eadie Mcdaniel           M1                        1   \n",
       "40        4541         Eadie Mcdaniel           M3                        3   \n",
       "41        4556           Shoaib Busby           M1                        2   \n",
       "42       41451       Sadiyah Whitaker           M1                        1   \n",
       "43      444545       Haidar Mccormick           M1                        1   \n",
       "44      444545       Haidar Mccormick           M3                        3   \n",
       "45      444545       Haidar Mccormick           M5                        0   \n",
       "46     4212121         Jarrad Ratliff           M1                        0   \n",
       "47     7486748              Jade Dean           M1                        4   \n",
       "48     7486748              Jade Dean           M3                        1   \n",
       "49     7486748              Jade Dean           M5                        0   \n",
       "50        4245         Pauline Norton           M1                        2   \n",
       "51        7795           Euan Mcguire           M1                        1   \n",
       "52        7795           Euan Mcguire           M3                        2   \n",
       "53         122          May Wilkerson           M1                        3   \n",
       "54         122          May Wilkerson           M3                        4   \n",
       "\n",
       "    Number of Runs               Salary Estimate First Name  Title  \\\n",
       "0               82  $137K-$171K (Glassdoor est.)       Maud    Mr.   \n",
       "1               71  $137K-$171K (Glassdoor est.)       Maud    Mr.   \n",
       "2               36  $137K-$171K (Glassdoor est.)       Maud    Mr.   \n",
       "3               76  $137K-$171K (Glassdoor est.)       Maud    Mr.   \n",
       "4                4   $75K-$131K (Glassdoor est.)      Logan   Sir.   \n",
       "5               38   $75K-$131K (Glassdoor est.)      Logan   Sir.   \n",
       "6               57  $100K-$131K (Glassdoor est.)      Avaya    Ms.   \n",
       "7               76  $100K-$131K (Glassdoor est.)      Avaya    Ms.   \n",
       "8               28  $100K-$131K (Glassdoor est.)      Avaya    Ms.   \n",
       "9               87   $50K-$101K (Glassdoor est.)     Marlie  Miss.   \n",
       "10              58   $50K-$101K (Glassdoor est.)     Marlie  Miss.   \n",
       "11             100   $50K-$101K (Glassdoor est.)     Marlie  Miss.   \n",
       "12              21  $101K-$165K (Glassdoor est.)       Onur    Mr.   \n",
       "13              58  $101K-$165K (Glassdoor est.)       Lyle    Mr.   \n",
       "14              38  $101K-$165K (Glassdoor est.)       Lyle    Mr.   \n",
       "15              98  $101K-$165K (Glassdoor est.)       Lyle    Mr.   \n",
       "16              72  $101K-$165K (Glassdoor est.)        Lea   Mrs.   \n",
       "17              58  $101K-$165K (Glassdoor est.)     Kaylee   Mrs.   \n",
       "18              82  $101K-$165K (Glassdoor est.)     Kaylee   Mrs.   \n",
       "19              18  $101K-$165K (Glassdoor est.)     Kaylee   Mrs.   \n",
       "20              33  $101K-$165K (Glassdoor est.)     Amayah    Ms.   \n",
       "21              94  $101K-$165K (Glassdoor est.)     Amayah    Ms.   \n",
       "22              89  $101K-$165K (Glassdoor est.)     Amayah    Ms.   \n",
       "23              31  $101K-$165K (Glassdoor est.)     Amayah    Ms.   \n",
       "24              62  $101K-$165K (Glassdoor est.)    Dorothy   Mrs.   \n",
       "25               6  $101K-$165K (Glassdoor est.)    Dorothy   Mrs.   \n",
       "26              91  $101K-$165K (Glassdoor est.)    Dorothy   Mrs.   \n",
       "27              68  $101K-$165K (Glassdoor est.)    Dorothy   Mrs.   \n",
       "28              16  $101K-$165K (Glassdoor est.)      Misty   (NA)   \n",
       "29              57  $101K-$165K (Glassdoor est.)      Misty   (NA)   \n",
       "30              80  $101K-$165K (Glassdoor est.)      Misty   (NA)   \n",
       "31              32  $122K-$146K (Glassdoor est.)     Mikael   (NA)   \n",
       "32              48  $122K-$146K (Glassdoor est.)     Mikael   (NA)   \n",
       "33              47  $122K-$146K (Glassdoor est.)     Mikael   (NA)   \n",
       "34              11  $138K-$158K (Glassdoor est.)  Nathaniel   (NA)   \n",
       "35              57    $145K-$225K(Employer est.)      Ameer   (NA)   \n",
       "36              37    $145K-$225K(Employer est.)      Ameer   (NA)   \n",
       "37               1    $145K-$225K(Employer est.)      Ameer   (NA)   \n",
       "38              92    $145K-$225K(Employer est.)      Ameer   (NA)   \n",
       "39              61   $69K-$116K (Glassdoor est.)      Eadie   (NA)   \n",
       "40               9   $69K-$116K (Glassdoor est.)      Eadie   (NA)   \n",
       "41             100   $79K-$106K (Glassdoor est.)     Shoaib   (NA)   \n",
       "42              74   $79K-$133K (Glassdoor est.)    Sadiyah   (NA)   \n",
       "43               3   $92K-$155K (Glassdoor est.)     Haidar   (NA)   \n",
       "44              34   $92K-$155K (Glassdoor est.)     Haidar   (NA)   \n",
       "45              51   $92K-$155K (Glassdoor est.)     Haidar   (NA)   \n",
       "46              58  $101K-$165K (Glassdoor est.)     Jarrad   (NA)   \n",
       "47               7  $110K-$163K (Glassdoor est.)       Jade   (NA)   \n",
       "48              20  $110K-$163K (Glassdoor est.)       Jade   (NA)   \n",
       "49              25  $110K-$163K (Glassdoor est.)       Jade   (NA)   \n",
       "50              77  $128K-$201K (Glassdoor est.)    Pauline   (NA)   \n",
       "51              53  $138K-$158K (Glassdoor est.)       Euan   (NA)   \n",
       "52              15  $138K-$158K (Glassdoor est.)       Euan   (NA)   \n",
       "53              89  $212K-$331K (Glassdoor est.)        May   (NA)   \n",
       "54              54  $212K-$331K (Glassdoor est.)        May   (NA)   \n",
       "\n",
       "     Last Name  Minimum_Salary  Maximum_Salary  Average_Salary          Team  \\\n",
       "0    Nicholson             137             171             154       England   \n",
       "1    Nicholson             137             171             154       England   \n",
       "2    Nicholson             137             171             154       England   \n",
       "3    Nicholson             137             171             154       England   \n",
       "4       Thomas              75             131             103         India   \n",
       "5       Thomas              75             131             103         India   \n",
       "6       Burris             100             131             116     Australia   \n",
       "7       Burris             100             131             116     Australia   \n",
       "8       Burris             100             131             116     Australia   \n",
       "9       Stacey              50             101              76   New Zealand   \n",
       "10      Stacey              50             101              76   New Zealand   \n",
       "11      Stacey              50             101              76   New Zealand   \n",
       "12     Bateman             101             165             133  South Africa   \n",
       "13      Mullen             101             165             133      Pakistan   \n",
       "14      Mullen             101             165             133      Pakistan   \n",
       "15      Mullen             101             165             133      Pakistan   \n",
       "16    Driscoll             101             165             133      Srilanka   \n",
       "17        Pitt             101             165             133       England   \n",
       "18        Pitt             101             165             133       England   \n",
       "19        Pitt             101             165             133       England   \n",
       "20      Baxter             101             165             133         India   \n",
       "21      Baxter             101             165             133         India   \n",
       "22      Baxter             101             165             133         India   \n",
       "23      Baxter             101             165             133         India   \n",
       "24    Guerrero             101             165             133     Australia   \n",
       "25    Guerrero             101             165             133     Australia   \n",
       "26    Guerrero             101             165             133     Australia   \n",
       "27    Guerrero             101             165             133     Australia   \n",
       "28       Dunne             101             165             133   New Zealand   \n",
       "29       Dunne             101             165             133   New Zealand   \n",
       "30       Dunne             101             165             133   New Zealand   \n",
       "31  Sutherland             122             146             134  South Africa   \n",
       "32  Sutherland             122             146             134  South Africa   \n",
       "33  Sutherland             122             146             134  South Africa   \n",
       "34      Gaines             138             158             148      Pakistan   \n",
       "35      Aldred             145             225             185      Pakistan   \n",
       "36      Aldred             145             225             185      Pakistan   \n",
       "37      Aldred             145             225             185      Pakistan   \n",
       "38      Aldred             145             225             185      Pakistan   \n",
       "39    Mcdaniel              69             116              93       England   \n",
       "40    Mcdaniel              69             116              93       England   \n",
       "41       Busby              79             106              93         India   \n",
       "42    Whitaker              79             133             106     Australia   \n",
       "43   Mccormick              92             155             124      Pakistan   \n",
       "44   Mccormick              92             155             124      Pakistan   \n",
       "45   Mccormick              92             155             124      Pakistan   \n",
       "46     Ratliff             101             165             133      Srilanka   \n",
       "47        Dean             110             163             137      Srilanks   \n",
       "48        Dean             110             163             137      Srilanks   \n",
       "49        Dean             110             163             137      Srilanks   \n",
       "50      Norton             128             201             165         India   \n",
       "51     Mcguire             138             158             148     Australia   \n",
       "52     Mcguire             138             158             148     Australia   \n",
       "53   Wilkerson             212             331             272   New Zealand   \n",
       "54   Wilkerson             212             331             272   New Zealand   \n",
       "\n",
       "           Role  Batting strike rate  Age  Captain  Bowling economy rate  \n",
       "0       Batsman                  110   24        1                   5.2  \n",
       "1       Batsman                  110   24        1                   5.2  \n",
       "2       Batsman                  110   24        1                   5.2  \n",
       "3       Batsman                  110   24        1                   5.2  \n",
       "4       Batsman                  130   32        1                   4.0  \n",
       "5       Batsman                  130   32        1                   4.0  \n",
       "6        Bowler                  105   35        1                   4.8  \n",
       "7        Bowler                  105   35        1                   4.8  \n",
       "8        Bowler                  105   35        1                   4.8  \n",
       "9   All-rounder                   80   27        1                   4.0  \n",
       "10  All-rounder                   80   27        1                   4.0  \n",
       "11  All-rounder                   80   27        1                   4.0  \n",
       "12      Batsman                   67   21        1                   3.0  \n",
       "13       Bowler                   95   24        1                   2.5  \n",
       "14       Bowler                   95   24        1                   2.5  \n",
       "15       Bowler                   95   24        1                   2.5  \n",
       "16  All-rounder                  200   26        1                   6.0  \n",
       "17      Batsman                  170   28        0                   7.2  \n",
       "18      Batsman                  170   28        0                   7.2  \n",
       "19      Batsman                  170   28        0                   7.2  \n",
       "20       Bowler                  145   29        0                   5.2  \n",
       "21       Bowler                  145   29        0                   5.2  \n",
       "22       Bowler                  145   29        0                   5.2  \n",
       "23       Bowler                  145   29        0                   5.2  \n",
       "24      Batsman                  120   30        0                   4.0  \n",
       "25      Batsman                  120   30        0                   4.0  \n",
       "26      Batsman                  120   30        0                   4.0  \n",
       "27      Batsman                  120   30        0                   4.0  \n",
       "28       Bowler                  100   31        0                   4.8  \n",
       "29       Bowler                  100   31        0                   4.8  \n",
       "30       Bowler                  100   31        0                   4.8  \n",
       "31  All-rounder                  105   32        0                   4.0  \n",
       "32  All-rounder                  105   32        0                   4.0  \n",
       "33  All-rounder                  105   32        0                   4.0  \n",
       "34      Batsman                  110   33        0                   3.0  \n",
       "35      Batsman                  115   34        0                   2.5  \n",
       "36      Batsman                  115   34        0                   2.5  \n",
       "37      Batsman                  115   34        0                   2.5  \n",
       "38      Batsman                  115   34        0                   2.5  \n",
       "39       Bowler                  120   35        0                   6.0  \n",
       "40       Bowler                  120   35        0                   6.0  \n",
       "41  All-rounder                   80   36        0                   7.2  \n",
       "42      Batsman                   70   38        0                   5.2  \n",
       "43  All-rounder                   75   21        0                   4.0  \n",
       "44  All-rounder                   75   21        0                   4.0  \n",
       "45  All-rounder                   75   21        0                   4.0  \n",
       "46      Batsman                  135   22        0                   3.0  \n",
       "47       Bowler                  175   23        0                   2.5  \n",
       "48       Bowler                  175   23        0                   2.5  \n",
       "49       Bowler                  175   23        0                   2.5  \n",
       "50      Batsman                  180   24        0                   6.0  \n",
       "51       Bowler                  105   28        0                   7.2  \n",
       "52       Bowler                  105   28        0                   7.2  \n",
       "53  All-rounder                  110   26        0                   5.2  \n",
       "54  All-rounder                  110   26        0                   5.2  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inner_merged=pd.merge(df,player_info_df,on=['Player Code','Player Name'],how='inner')\n",
    "inner_merged"
   ]
  }
 ],
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